Lecture 10 : Whole genome sequencing and analysis Lecture 10 : Whole genome sequencing and analysis

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Text of Lecture 10 : Whole genome sequencing and analysis Lecture 10 : Whole genome sequencing and analysis

  • Lecture 10 : Whole genome sequencing and analysis Introduction to Computational

    Biology Teresa Przytycka, PhD

  • Sequencing DNA •  Goal – obtain the string of bases that make a given

    DNA strand. •  Problem –Typically one cans sequence directly

    only DNA of short length (400-700 bp – Sanger;

  • Cutting and breaking DNA •  Restriction enzymes – proteins that catalyze hydrolysis

    (breaking the molecule by adding water) of DNA at certain points called restriction sides.

    •  Example: EcoRI restriction side GAATTC. Note that the complement of GAATTC is GAATTC (a sequence equal to its reverse is called a palindrome)

    …ATCCAGAATTCTC… …TAGGTCTTAAGAG

    ATCCAG AATTCTC…

    …TAGGTCTTAA AG

  • Fragment assembly

    •  After DNA fragments (reads) are sequenced we want to assemble then together to reconstruct the entire target sequence.

    •  If the overlaps were unique and error free, this would be relatively easy task… but they are not.

    •  In addition : fragments can come from any of the two DNA strands and we do not know which

  • The “ideal” example

    Input: ACCGT CGTGC TTAC TACCGT Assume target sequence of about 10bp. - - ACCGT - - - - CGTGC TTAC - - - - - - TACCGT - - TTACCGTGC consensus sequence

    Sample overlaps

  • Fragment assembly

    •  After DNA fragments (reads) are sequenced we want to assemble then together to reconstruct the entire target sequence.

    •  Most fragment assembly algorithms include the following 3 steps: –  Overlap - finding potentially overlapping fragments –  Layout – finding the order of the fragments –  Consensus – deriving DNA sequence from the layout.

    •  Usually we know with some approximation the length of the target sequence.

  • Finding overlaps

    •  In theory we should test for overlaps all pairs of fragments. For every pair we will consider all relative orientations.

    •  One possible method: perform alignment without charging for flanking gaps

    - - TAATG TGTAA - -

  • Representing overlaps F - fragments. Overlap graph : vertices = elements of F weighted edges: if a, b ∈ F then the weight of

    edge from a to b is equal t where maximum integer such that

    suffix(a,t) = prefix(b,t) suffix(a,t) = last t symbols of a prefix(b,t) = first t symbols of b

    Path dbc leads to alignment

    Path abcd leads to alignment

    a

    b

    d

    c

    Each simple path (simple = not using the same vertex more than once) in overlap graph defines an alignment. Two assumptions: - no fragment completely included in another - Direction of fragments is known

  • Finding Layout

    Definition: Hamiltonian path – a path that visits each vertex exactly once.

    Let P – path, A the set of fragments involved in A |S(P)| = ||A|| - w(P) Where ||A|| sum of lengths of fragments in

    A w(P) the sum of weight of path P (sum of

    the edge weights on this paths).

  • The greedy algorithm

    •  Goal: find a Hamiltonian path with large w(P).

    •  Heuristic: iteratively find the heavies edge and try to add it to the path:

    •  Acceptance test: An edge can be added to the path, if it will not create brunching point on the path.

  • Algorithm Greedy: sort edges by weight for each edge (f,g) in decreasing order

    perform acceptance test for (f,g) if accepted add it to the path

    Example: greedy choice Try: (a,d) – ok, selected Try: (d,b) – ok, selected Try: (a,b) – acceptance test false Try: (b,c) – ok, selected

    a

    b

    d

    c

    From Setubal/Meidanis book

  • Complication - repeated regions Repeated regions: sequences that appears more than once in the molecule. The

    copies of repeats do not need to be exactly the same. Problems are illustrated below:

    From Setubal/Meidanis book

  • Coverage and linkage •  coverage = number of times given position is

    included in a an aligned fragment. •  if a coverage equals 0 at some column – we do not

    have continuous layout. •  linkage amount of overlaps between fragments:

    From Setubal/Meidanis book

  • Complication – lack of coverage

    •  Coverage at position i of the target is the number of fragments that cover this position.

    •  A conting – continuously covered region.

    Target DNA uncovered area

  • Closing gaps •  sequence walking (direct sequencing)

    -  derive a primer from a sequence near the end of a conting -  replicate the sequence starting at the primer -  sequence this the replicated sequence -  if the replicated sequence did not cover the gap, repeat the above steps. - Problems: tedious for larger gap, region of interest must be unique in the genome

    • dual end sequencing. Recall that the inserts are much longer than the sequenced fragments. If we sequence both ends of the insert, we obtain mate pairs which can be used as follows: if two ends of a mate pair are in two different contigs, we can deduce the orientation and distance between two contings. Scaffold – sequence of contigs where the order and distances between the contigs are approximately known.,

  • What do we learn form whole genome sequence

    •  Using gene finding algorithm we can discover significant portion of genes

    •  Understand the structure of a genome •  Understand genome evolution •  Searching for genes associated with

    diseases

  • Genome duplication

    •  Gene duplication – widely accepted method for creation of new genes

    •  Ohno proposes that whole genome duplication (polyploidization) provides material for new genomes (1970)

    •  2R Hypothesis: two rounds of polyploidization followed by gene loss and functional divergence occurred early in vertebrate lineage.

  • Syntheny blocks

    Results filtered to report segments at least 1000bp, at lest 59% identity

    NATURE 1 VOL 40S 114 DECEMBER 20001 www.nature.com 801

    In comparative genome analysis synteny blocks = regions containing the homologous genes Below: Segmental duplications in the Arabidopsis genome fund using program MUMer.

  • How many rounds of genome duplication?

    •  Two round of genome duplication should lead to occurrences of groups of four synteny blocks

    •  Such tree should be then observed in the current genome

    •  They should be consistent •  For vertebrates evolution there is

    evidence for full genome duplication

    A B C D

  • Whole genome duplications in yeast

  • Computational Approach •  Find syntheny blocks •  Find overlaps in syntheny blocks •  Use duplicate synteny blocks do define “sister”

    regions in S. cerevisiae (145 sister regions covering 88% of the genome)

  • Some lessons from whole genome alignment of closely related species

  • Neutral evolution/natural selection •  natural selection: a process by which biological populations are

    altered over time, as a result of the propagation of heritable traits that affect the capacity of individual organisms to survive. –  responsible for organisms being adapted to their environment. –  The theory of natural selection was proposed by Charles Darwin and Alfred

    Russel Wallace in 1858, though vaguer and more obscure formulations had been arrived at by earlier researchers.

    •  neutral theory of evolution (Kimura 1960): –  vast majority of molecular differences are selectively neutral. –  these genome features are neither subject to, nor explicable by, natural

    selection. –  most evolutionary change is the result of genetic drift acting on neutral alleles.

    Through drift, these new alleles may become more common within the population. They may subsequently decline and disappear, or in rare cases they may become fixed--meaning that the substitution they carry becomes a universal feature of the population or species

    •  The neutralist-selectionist debate – which is the prevalent evolutionary force?

  • Comparative Genome analysis tools

    Assume two closely related organisms (closely for this purpose is that probability of a back substitutions A!X!A are unlikely: example muse/rat; human chimpanzee)

    KA - #of coding base substitutions that results in amino- acid change

    KS - of coding base substitutions that do not results in amino-acid change (synonymous substitution rate)

    KA/ KS – measure of evolutionary constraints KA/ KS 1; possible adaptive or positive selection

    KA / K S ratio

  • Comparison mouse/rat human/chimpanzee Initial sequence of the chimpanzee genome and comparison with Human

    genome, The Chimpanzee Genome Seque